In this work we focus on the numerical issues in the evaluation of an important class of financial derivatives: participating life insurance contracts. We investigate the impact of different numerical methods on accuracy and efficiency in the solution of main computational kernels generally arising from mathematical models describing the financial problem. The main kernels involved in the evaluation of these financial derivatives are multidimensional integrals and stochastic differential equations. For this reason we consider different Monte Carlo simulations and various stochastic differential equations discretization schemes. We have established that a combination of the Monte Carlo method with the Antithetic Variates variance reduction technique and the fully implicit Euler scheme developed by Brigo and Alfonsi allows to obtain high efficiency and good accuracy.

On software development for financial evaluation of Participating Life Insurance policies

CORSARO, STEFANIA;DE ANGELIS, Pasquale Luigi;MARINO, ZELDA;PERLA, Francesca
2007

Abstract

In this work we focus on the numerical issues in the evaluation of an important class of financial derivatives: participating life insurance contracts. We investigate the impact of different numerical methods on accuracy and efficiency in the solution of main computational kernels generally arising from mathematical models describing the financial problem. The main kernels involved in the evaluation of these financial derivatives are multidimensional integrals and stochastic differential equations. For this reason we consider different Monte Carlo simulations and various stochastic differential equations discretization schemes. We have established that a combination of the Monte Carlo method with the Antithetic Variates variance reduction technique and the fully implicit Euler scheme developed by Brigo and Alfonsi allows to obtain high efficiency and good accuracy.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/725
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